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{{AppliesToMachineLearning}}
{{AppliesTo Machine Learning TN}}
{{AppliesToMito8M}}
{{AppliesTo MITO 8M TN}}
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==Introduction==
This Technical Note (TN for short) belongs to the series introduced [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1|here]].
Specifically, it illustrates the execution of an inference application (fruit classifier) that makes use of the model described in [[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1#Reference_application_.231:_fruit_classifier|this sectioninference application (fruit classifier)]] when executed on the [[:Category:Mito8M|Mito8M SoM]], a system-on-module based on the NXP [https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-processors/i-mx-8m-family-armcortex-a53-cortex-m4-audio-voice-video:i.MX8M i.MX8M SoC].
=== Test bed ===
The kernel and the root file system of the tested platform were built with the L4.14.98_2.0.0 release of the Yocto Board Support Package for i.MX 8 family of devices. They were built with support for [https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ eIQ]: "a collection of software and development tools for NXP microprocessors and microcontrollers to do inference of neural network models on embedded systems".
The following table details the relevant specs of the test bed. {| class="wikitable" style="margin: auto;"
|-
|'''NXP Linux BSP release'''
|TensorFlow Lite 1.12
|-
|'''Maximum ARM cores frequency (max)'''
'''[MHz]'''
|[[File:ML - TF1.15QAT fruitsmodel.png|none|thumb|1000x1000px]]
|}
The following images show the graphs of the models after conversion (click to enlarge):
2.47029e-18 Hand
</pre>
The following screenshot shows the system status while executing the application. In this case, the thread parameter was unspecified.
== Results ==
The following table lists the prediction times for a single image depending on the model and the thread parameter.
{| class="wikitable" style="margin: auto;"
|unspecified
|200
|Four threads are created beside the main process (supposedly, this quantity is set accordingly to the number of physical cores available). Nevertheless, they seem to be constantly in sleep state.
|-
|4
|8084|Interestingly, 7 actual processes are created besides beside the main one. Four of them, however, seem to be constantly in sleep state.
|}
The prediction time '''takes into account the time needed to fill the input tensor with the image'''. Furthermore, it is averaged over several predictions.
The same tests were repeated using a network file system (NFS) over an Ethernet connection, too. No significant variations in the prediction times were observed.
In conclusion, to maximize the performance in terms of execution time, the model has to be fully-quantized and the number of threads has to be specified explicitly.